Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?

Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der Waals structures that enable ease of integration with conventional electronic materials and silicon-based hardware. All major classes of non-volatile memory (NVM) devices have been demonstrated using 2D materials, including their operation as synaptic devices for applications in neuromorphic computing hardware. Their atomically-thin structure, superior physical properties, i.e., mechanical strength, electrical and thermal conductivity, as well as gate-tunable electronic properties provide performance advantages and novel functionality in NVM devices and systems. However, device performance and variability as compared to incumbent materials and technology remain major concerns for real applications. Ultimately, the progress of 2D materials as a novel class of electronic materials and specifically their application in the area of neuromorphic electronics will depend on their scalable synthesis in thin-film form with desired crystal quality, defect density, and phase purity.Comment: Neuromorphic Computing, 2D Materials, Heterostructures, Emerging Memory Devices, Resistive, Phase-Change, Ferroelectric, Ferromagnetic, Crossbar Array, Machine Learning, Deep Learning, Spiking Neural Network

Analog Content-Addressable Memory from Complementary FeFETs

To address the increasing computational demands of artificial intelligence (AI) and big data, compute-in-memory (CIM) integrates memory and processing units into the same physical location, reducing the time and energy overhead of the system. Despite advancements in non-volatile memory (NVM) for matrix multiplication, other critical data-intensive operations, like parallel search, have been overlooked. Current parallel search architectures, namely content-addressable memory (CAM), often use binary, which restricts density and functionality. We present an analog CAM (ACAM) cell, built on two complementary ferroelectric field-effect transistors (FeFETs), that performs parallel search in the analog domain with over 40 distinct match windows. We then deploy it to calculate similarity between vectors, a building block in the following two machine learning problems. ACAM outperforms ternary CAM (TCAM) when applied to similarity search for few-shot learning on the Omniglot dataset, yielding projected simulation results with improved inference accuracy by 5%, 3x denser memory architecture, and more than 100x faster speed compared to central processing unit (CPU) and graphics processing unit (GPU) per similarity search on scaled CMOS nodes. We also demonstrate 1-step inference on a kernel regression model by combining non-linear kernel computation and matrix multiplication in ACAM, with simulation estimates indicating 1,000x faster inference than CPU and GPU

Tuning Polarity in WSe₂/AlScN FeFETs via Contact Engineering

Recent advancements in ferroelectric field-effect transistors (FeFETs) using two-dimensional (2D) semiconductor channels and ferroelectric Al0.68Sc0.32N (AlScN) allow high-performance nonvolatile devices with exceptional ON-state currents, large ON/OFF current ratios, and large memory windows (MW). However, previous studies have solely focused on n-type FeFETs, leaving a crucial gap in the development of p-type and ambipolar FeFETs, which are essential for expanding their applicability to a wide range of circuit-level applications. Here, we present a comprehensive demonstration of n-type, p-type, and ambipolar FeFETs on an array scale using AlScN and multilayer/monolayer WSe2. The dominant injected carrier type is modulated through contact engineering at the metal–semiconductor junction, resulting in the realization of all three types of FeFETs. The effect of contact engineering on the carrier injection is further investigated through technology-computer-aided design simulations. Moreover, our 2D WSe2/AlScN FeFETs achieve high electron and hole current densities of ∼20 and ∼10 μA/μm, respectively, with a high ON/OFF ratio surpassing ∼107 and a large MW of >6 V (0.14 V/nm)